Survival analysis and regression in infectious disease epidemiology

传染病流行病学中的生存分析和回归

• 批准号: 8535600
• 负责人: Eben Kenah
• 金额: $ 21.92万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-07 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8535600
• 关键词: Bayesian Analysis; Behavior; Biometry; Chronic Disease; Code; Communicable Diseases; Communities; Confidence Intervals; Data; Data Analyses; Data Set; Disease Outbreaks; Epidemic; Epidemiology; Evaluation; Event; Genetic; Household; Individual; Infection; Infectious Disease Epidemiology; Investigation; Joints; Life; Markov Chains; Methods; Modeling; Observational Study; Persons; Phase; Phylogenetic Analysis; Population; Predisposition; Research; Research Design; Sampling; Source; Statistical Methods; Sum; Survival Analysis; System; Testing; Time; Trees; Vaccines; abstracting; analytical method; base; experience; flexibility; hazard; novel; pathogen; simulation; theories; transmission process

Project summary/abstract The project aims to develop more flexible and powerful statistical methods for infectious disease epidemiology based on survival analysis and contact intervals. The contact interval from person A to person B is the time between the onset of infectiousness in A and infectious contact from A to B, where infectious contact is defined to be a contact sufficient to infect a susceptible person. It can be right-censored if B is infected by someone other than A or if A recovers from infectiousness prior to infectious contact with B. Because of this censoring, survival analysis is the natural approach to estimating the contact interval distribution. During the K99 phase, this project will develop nonparametric estimates of the contact interval distribution and semiparametric regression models allowing the estimation of log hazard ratios representing the effects of covariates on infectiousness and susceptibility. Since many of these estimates can be represented as sums or averages over possible transmission trees, they provide a natural bridge between traditional epidemic data and phylogenetic data. During the R00 phase, generalizations of the K99 methods will be developed and placed into a Bayesian framework for the analysis of partially-observed epidemics. Survival analysis is one of the pillars of modern biostatistics, so it will be a rich source of novel study designs and analytical methods in infectious disease epidemiology.

项目总结/文摘